The 3D SIFT-Rank keypoint method we use is a robust and well-established baseline, however our distance measure may generally be used with any keypoint representation. The search for new keypoint detectors and descriptors remains an active research focus, primarily in the context of 2D computer vision. We refer the reader to an extensive literature review of traditional hand-crafted solutions [1]} and a recent discussion of 2D SIFT matching technology [2]}. Deep learning keypoint methods have been investigated, examples include LIFT [3]}, DISK [4]}, LF-Net [5]}, SuperPoint [6]}. Nevertheless, variants of the original SIFT histogram descriptor, including SIFT-Rank [7]}, DSP-SIFT [8]} or RootSIFT [9]} remain competitive with descriptors derived from training [10]}, [11]}, particularly for non-planar objects [2]}, while requiring no training and few hyperparameters. Learning-based approaches often rely on hand-crafted keypoint detectors to generate training data [3]}, [6]}. The challenges of developing deep keypoint architectures include training of individual components (i.e. keypoint detector, orientation estimator, descriptor), achieving invariance to rotation or scale changes, the need to retrain for different imaging modalities and body parts, and avoiding bias due to training data procedures [15]}.
